Beginner's Guide to Streamlit with Python : Build Web-Based Data and Machine Learning Applications

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You’ll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you’ll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. What You Will Learn How to start developing web applications using Streamlit What are Streamlit's components Media elements in Streamlit How to visualize data using various interactive and dynamic Python libraries How to implement models in Streamlit web applications Who This Book Is For Professionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of web development. Table of Contents 5 About the Author 12 About the Technical Reviewer 13 Acknowledgments 14 Introduction 15 Chapter 1: Introduction to Streamlit 18 What Is Streamlit? 18 Why Streamlit? 19 Why Streamlit for Data Science and ML Engineers? 19 Features of Streamlit 20 Open Source 20 Platforms 20 Ease of Development 20 Interactive Applications 20 Reduced Time of Development 20 No Core Web Development Knowledge 21 Easy to Learn 21 Model Implementation 21 Compatibility 21 Literate Programming Document 22 Streamlit Cloud 22 Optimize Change 22 Error Notifications 23 Comparing Streamlit to Alternative Frameworks 23 Installing Python 25 Installing Streamlit on Windows 26 Installing Streamlit on Linux 26 Installing Streamlit on macOS 26 Testing the Streamlit Installation 27 Creating Our First App 29 Summary 32 Chapter 2: Text and Table Elements 33 Text Elements 33 Titles 34 Headers 35 Subheaders 36 Captions 37 Plain Text 39 Markdown 40 LaTeX 41 Code 42 Data Elements 45 Dataframes 45 Tables 48 Metrics 50 JSON 52 The write() Function as a Superfunction 54 Magic 58 Summary 61 Chapter 3: Visualization 62 The Importance of Visualization 62 Visualization in Streamlit 62 Purpose of Visualization 63 Streamlit Functions 63 Bar 63 Line 65 Area 66 Map 68 Graphviz 69 Seaborn 71 Count 72 Violin 73 Strip 74 Altair 75 Boxplot 76 Area 77 Heatmap 78 Plotly 80 Pie 80 Donut 82 Scatter 83 Line 85 Bar 86 Bar Horizontal 89 Subplots 90 Summary 92 Chapter 4: Data and Media Elements 94 Images 94 Multiple Images 99 Background Image 102 Resizing an Image 103 Audio 105 Video 107 Balloon 110 Snowflake 111 Emojis 112 Summary 113 Chapter 5: Buttons and Sliders 114 Buttons 114 Radio Buttons 115 Check Boxes 117 Drop-Downs 119 Multiselects 121 Download Buttons 123 Progress Bars 124 Spinners 126 Summary 127 Chapter 6: Forms 128 Text Box 128 Text Area 132 Number Input 133 Time 135 Date 136 Color 138 File Upload 140 Text/Docx Document 141 PDF Upload 146 Dataset Upload 148 Image Upload 150 Uploading Multiple Images 151 Saving Uploaded Documents 153 Submit Button 157 Summary 158 Chapter 7: Columns and Navigation 159 Columns 159 Spaced-Out Columns 160 Columns with Padding 162 Grids 164 Expanders/Accordions 165 Containers 167 Empty Containers 169 Sidebars 170 Multipage Navigation 171 Main Page 172 Pages 172 Summary 174 Chapter 8: Control Flow and Advanced Features 175 Alert Box 175 st.info() 175 st.warning() 176 st.success() 176 st.error() 176 st.exception() 176 Control Flow 177 Stop Execution 178 Rerun the Script 178 st.form_submit_button 179 Advanced Features 180 Configuring the Page 180 st.echo 181 st.experimental_show 182 Session State 183 Performance 185 Caching 185 st.experimental_memo 186 st.experimental_memo.clear() 187 st.experimental_singleton 187 st.experimental_singleton.clear 187 Summary 188 Chapter 9: Natural Language Processing 189 NLP App Creation 189 User Input 190 Cleaning the Text 190 Predictions 191 Setting Up Files 194 Requirement Text 195 setup.sh 195 Procfile 196 GitHub Repository Creation 197 Heroku 197 Deployment 199 Summary 201 Chapter 10: Computer Vision in Streamlit 202 Installing Libraries 202 Model Deployment 203 Upload Image 203 Map Image Classes 204 Apply Imaging Techniques 204 Model Preprocessing 205 Predictions 205 Complete Code 206 Index 209 This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. You will: Start developing web applications using Streamlit Understand Streamlit's components Utilize media elements in Streamlit Visualize data using various interactive and dynamic Python libraries Implement models in Streamlit web applications